Traffic Transformer: Transformer-based framework for temporal traffic accident prediction
<p dir="ltr">Reliable prediction of traffic accidents is crucial for the identification of potential hazards in advance, formulation of effective preventative measures, and reduction of accident incidence. Existing neural network-based models generally suffer from a limited field of...
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2024
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| _version_ | 1864513510219710464 |
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| author | Mansoor G. Al-Thani (19237204) |
| author2 | Ziyu Sheng (19237207) Yuting Cao (4231810) Yin Yang (35103) |
| author2_role | author author author |
| author_facet | Mansoor G. Al-Thani (19237204) Ziyu Sheng (19237207) Yuting Cao (4231810) Yin Yang (35103) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mansoor G. Al-Thani (19237204) Ziyu Sheng (19237207) Yuting Cao (4231810) Yin Yang (35103) |
| dc.date.none.fl_str_mv | 2024-04-01T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3934/math.2024617 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Traffic_Transformer_Transformer-based_framework_for_temporal_traffic_accident_prediction/26389384 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Mathematical sciences Applied mathematics traffic accident prediction deep learning transformer attention mechanism neural network |
| dc.title.none.fl_str_mv | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Reliable prediction of traffic accidents is crucial for the identification of potential hazards in advance, formulation of effective preventative measures, and reduction of accident incidence. Existing neural network-based models generally suffer from a limited field of perception and poor long-term dependency capturing abilities, which severely restrict their performance. To address the inherent shortcomings of current traffic prediction models, we propose the Traffic Transformer for multidimensional, multi-step traffic accident prediction. Initially, raw datasets chronicling sporadic traffic accidents are transformed into multivariate, regularly sampled sequences that are amenable to sequential modeling through a temporal discretization process. Subsequently, Traffic Transformer captures and learns the hidden relationships between any elements of the input sequence, constructing accurate prediction for multiple forthcoming intervals of traffic accidents. Our proposed Traffic Transformer employs the sophisticated multi-head attention mechanism in lieu of the widely used recurrent architecture. This significant shift enhances the model's ability to capture long-range dependencies within time series data. Moreover, it facilitates a more flexible and comprehensive learning of diverse hidden patterns within the sequences. It also offers the versatility of convenient extension and transference to other diverse time series forecasting tasks, demonstrating robust potential for further development in this field. Extensive comparative experiments conducted on a real-world dataset from Qatar demonstrate that our proposed Traffic Transformer model significantly outperforms existing mainstream time series forecasting models across all evaluation metrics and forecast horizons. Notably, its Mean Absolute Percentage Error reaches a minimal value of only 4.43%, which is substantially lower than the error rates observed in other models. This remarkable performance underscores the Traffic Transformer's state-of-the-art level of in predictive accuracy.</p><h2>Other Information</h2><p dir="ltr">Published in: AIMS Mathematics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3934/math.2024617" target="_blank">https://dx.doi.org/10.3934/math.2024617</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5a458caaa428ed2ff49ce9179fe46492 |
| identifier_str_mv | 10.3934/math.2024617 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26389384 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Traffic Transformer: Transformer-based framework for temporal traffic accident predictionMansoor G. Al-Thani (19237204)Ziyu Sheng (19237207)Yuting Cao (4231810)Yin Yang (35103)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningMathematical sciencesApplied mathematicstraffic accident predictiondeep learningtransformerattention mechanismneural network<p dir="ltr">Reliable prediction of traffic accidents is crucial for the identification of potential hazards in advance, formulation of effective preventative measures, and reduction of accident incidence. Existing neural network-based models generally suffer from a limited field of perception and poor long-term dependency capturing abilities, which severely restrict their performance. To address the inherent shortcomings of current traffic prediction models, we propose the Traffic Transformer for multidimensional, multi-step traffic accident prediction. Initially, raw datasets chronicling sporadic traffic accidents are transformed into multivariate, regularly sampled sequences that are amenable to sequential modeling through a temporal discretization process. Subsequently, Traffic Transformer captures and learns the hidden relationships between any elements of the input sequence, constructing accurate prediction for multiple forthcoming intervals of traffic accidents. Our proposed Traffic Transformer employs the sophisticated multi-head attention mechanism in lieu of the widely used recurrent architecture. This significant shift enhances the model's ability to capture long-range dependencies within time series data. Moreover, it facilitates a more flexible and comprehensive learning of diverse hidden patterns within the sequences. It also offers the versatility of convenient extension and transference to other diverse time series forecasting tasks, demonstrating robust potential for further development in this field. Extensive comparative experiments conducted on a real-world dataset from Qatar demonstrate that our proposed Traffic Transformer model significantly outperforms existing mainstream time series forecasting models across all evaluation metrics and forecast horizons. Notably, its Mean Absolute Percentage Error reaches a minimal value of only 4.43%, which is substantially lower than the error rates observed in other models. This remarkable performance underscores the Traffic Transformer's state-of-the-art level of in predictive accuracy.</p><h2>Other Information</h2><p dir="ltr">Published in: AIMS Mathematics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3934/math.2024617" target="_blank">https://dx.doi.org/10.3934/math.2024617</a></p>2024-04-01T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3934/math.2024617https://figshare.com/articles/journal_contribution/Traffic_Transformer_Transformer-based_framework_for_temporal_traffic_accident_prediction/26389384CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263893842024-04-01T03:00:00Z |
| spellingShingle | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction Mansoor G. Al-Thani (19237204) Information and computing sciences Artificial intelligence Data management and data science Machine learning Mathematical sciences Applied mathematics traffic accident prediction deep learning transformer attention mechanism neural network |
| status_str | publishedVersion |
| title | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction |
| title_full | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction |
| title_fullStr | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction |
| title_full_unstemmed | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction |
| title_short | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction |
| title_sort | Traffic Transformer: Transformer-based framework for temporal traffic accident prediction |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Mathematical sciences Applied mathematics traffic accident prediction deep learning transformer attention mechanism neural network |